Popular GitHub Repositories for Ucf

Video Platform for Action Recognition and Object Detection in Pytorch

Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"

This is an official implementation of TubeR: Tubelet Transformer for Video Action Detection

This repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).

Video Representation Learning by Recognizing Temporal Transformations. In ECCV, 2020.

Use 3D ResNet to extract features of UCF101 and HMDB51 and then classify them.

Transformer for Action Recognition in PyTorch

Various implementations and projects on CNN, RNN, LSTM, GAN, etc

TensorFlow implementation for "Guided Optical Flow Learning"

Implementation of LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN

Source Code for Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Surveillance Videos

Action Recognition using Convolutional Neural Network (CNN)

A pytorch implementation of a text to videos GAN

A simple and fun video classification/action recognition using VGG16 as a feature extractor and RNN.

SIGAI@UCF lectures/workshops, categorized by semester. (For spring 2018, will be archived soon.)

This repository contains my personal code for the paper Learning Spatiotemporal Features with 3D Convolutional Networks by Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri.

UCF-Crime-Anomaly-Detection

This project implements a novel architecture that combines Change Detection, Anomaly Detection, Clustering and Video Summarization for effectively summarizing lengthy CCTV footage around crime anomalies. Dataset: UCF-crime dataset

A deep learning project to predict and generate future video frames using models like ConvLSTM, PredRNN, and Transformers, leveraging the UCF101 dataset. The repository includes preprocessing, model training, video generation, an interactive UI, and evaluation metrics to compare model performance in video synthesis and temporal prediction tasks.

Efficient video action recognition using hybrid techniques: combining ORB, SIFT, and deep models like VideoMAE and (2+1)D Conv to reduce data size while maintaining performance.